class_docstr: An architecture combining hybrid Transformer/RNN with character-based
  inputs (token embeddings created via character-input CNN)
code: def build_decoder(cls, args, src_dict, dst_dict):
      decoder_embed_tokens = (
          pytorch_translate_transformer.build_embedding(
              dictionary=dst_dict,
              embed_dim=args.decoder_embed_dim,
              path=args.decoder_pretrained_embed,
              freeze=args.decoder_freeze_embed,
          )
      )
      return hybrid_transformer_rnn.HybridRNNDecoder(
          args, src_dict, dst_dict, decoder_embed_tokens
      )
  
imports: import logging
  import math
  from ast import literal_eval
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq.models import FairseqEncoder, register_model, register_model_architecture, transformer
  from fairseq.modules import SinusoidalPositionalEmbedding
  from pytorch_translate import char_encoder, char_source_model, hybrid_transformer_rnn, model_constants, rnn, transformer, utils, vocab_constants
  from pytorch_translate.common_layers import TransformerEncoderGivenEmbeddings, VariableTracker
  from pytorch_translate.data.dictionary import TAGS
label_desc: Use image node_id 6 for calling the CharSourceHybridModel obj's underlying member method code with example usage: obj.build_decoder(cls, args, src_dict, dst_dict) and returns: hybrid_transformer_rnn